Understanding Generalization in Node and Link Prediction

Using message-passing graph neural networks (MPNNs) for node and link prediction is crucial in various scientific and industrial domains, which has led to the development of diverse MPNN architectures. Besides working well in practical settings, their ability to generalize beyond the training set remains poorly understood. While some studies have explored the generalization of MPNNs in graph-level prediction tasks, much less attention has been given to node- and link-level predictions. Existing works often rely on unrealistic i.i.d. assumptions, overlooking possible correlations between nodes or links, and assuming fixed aggregation and impractical loss functions while neglecting the influence of graph structure. In this work, we introduce a unified framework for analyzing the generalization properties of MPNNs in inductive and transductive node and link prediction settings, incorporating diverse architectural parameters and loss functions, and quantifying the influence of graph structure. Additionally, our proposed generalization framework can be applied beyond graphs to any classification task, regardless of whether it is inductive or transductive. Our empirical study supports our theoretical insights, deepening our understanding of MPNNs' generalization capabilities in these tasks.